Improving tree-based models of software quality with principal components analysis

Software quality classification models can predict which modules are to be considered fault-prone, and which are not, based on software product metrics, process metrics and execution metrics. Such predictions can be used to target improvement efforts to those modules that need them the most. Classification-tree modeling is a robust technique for building such software quality models. However, the model structure may be unstable, and accuracy may suffer when the predictors are highly correlated. This paper presents an empirical case study of four releases of a very large telecommunications system, which shows that the tree-based models can be improved by transforming the predictors with principal components analysis, so that the transformed predictors are not correlated. The case study used the regression-tree algorithm in the S-Plus package and then applied a general decision rule to classify the modules.

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